Publications

Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience
Manfred Diaz
Liam Paull
Andrea Tacchetti
Teacher-Student Curriculum Learning (TSCL) is a curriculum learning framework that draws inspiration from human cultural transmission and le… (voir plus)arning. It involves a teacher algorithm shaping the learning process of a learner algorithm by exposing it to controlled experiences. Despite its success, understanding the conditions under which TSCL is effective remains challenging. In this paper, we propose a data-centric perspective to analyze the underlying mechanics of the teacher-student interactions in TSCL. We leverage cooperative game theory to describe how the composition of the set of experiences presented by the teacher to the learner, as well as their order, influences the performance of the curriculum that is found by TSCL approaches. To do so, we demonstrate that for every TSCL problem, there exists an equivalent cooperative game, and several key components of the TSCL framework can be reinterpreted using game-theoretic principles. Through experiments covering supervised learning, reinforcement learning, and classical games, we estimate the cooperative values of experiences and use value-proportional curriculum mechanisms to construct curricula, even in cases where TSCL struggles. The framework and experimental setup we present in this work represent a novel foundation for a deeper exploration of TSCL, shedding light on its underlying mechanisms and providing insights into its broader applicability in machine learning.
2024-09-17
TMLR (accepté)
doi.org
openreview.net
Deconvolving X-ray Galaxy Cluster Spectra Using a Recurrent Inference Machine
C. Rhea
Julie Hlavacek-larrondo
Alexandre Adam
Ralph P. Kraft
Ákos Bogdán
Laurence Perreault-Levasseur
Marine Prunier
Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new fr… (voir plus)ontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clusters from the instrumental response function. Employing state-of-the-art modeling software and data mining techniques of the Chandra data archive, we construct a set of 100,000 mock Chandra spectra. We train a recurrent inference machine (RIM) to take in the instrumental response and mock observation and output the intrinsic X-ray spectrum. The RIM can recover the mock intrinsic spectrum below the 1-
2024-09-16
ArXiv (prépublication)
arxiv.org
arxiv.org
Swarming Out of the Lab: Comparing Relative Localization Methods for Collective Behavior
Rafael Gomes Braga
Vivek Shankar Vardharajan
Giovanni Beltrame
David St-Onge
2024-09-15
Lecture Notes in Computer Science (publié)
doi.org
When Machines Outshine Humans in Object Recognition, Benchmarking Dilemma
Mohammad Javad Darvishi Bayazi
Md Rifat Arefin
Jocelyn Faubert
Irina Rish
2024-09-15
Journal of Vision (publié)
doi.org
A high-throughput phenotypic screen combined with an ultra-large-scale deep learning-based virtual screening reveals novel scaffolds of antibacterial compounds
Gabriele Scalia
Steven T. Rutherford
Ziqing Lu
Kerry R. Buchholz
Nicholas Skelton
Kangway Chuang
Nathaniel Diamant
Jan-Christian Hütter
Jerome-Maxim Luescher
Anh Miu
Jeff Blaney
Leo Gendelev
Elizabeth Skippington
Greg Zynda
Nia Dickson
Michał Koziarski
Yoshua Bengio
Aviv Regev
Man-Wah Tan
Tommaso Biancalani
2024-09-14
bioRxiv (prépublication)
doi.org
Burst firing optimizes invariant coding of natural communication signals by electrosensory neural populations
Michael G. Metzen
Amin Akhshi
Pouya Bashivan
Anmar Khadra
Maurice J. Chacron
2024-09-13
bioRxiv (prépublication)
doi.org
LMAC-TD: Producing Time Domain Explanations for Audio Classifiers
Eleonora Mancini
Francesco Paissan
Mirco Ravanelli
Cem Subakan
2024-09-13
ArXiv (prépublication)
doi.org
arxiv.org
Trimming the Risk: Towards Reliable Continuous Training for Deep Learning Inspection Systems
Altaf Allah Abbassi
Houssem Ben Braiek
Foutse Khomh
Thomas Reid
2024-09-13
ArXiv (prépublication)
doi.org
arxiv.org
End-to-end Conditional Robust Optimization
Abhilash Reddy Chenreddy
Erick Delage
2024-09-12
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence (publié)
proceedings.mlr.press
openreview.net
Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models
Lucas Berry
Axel Brando
David Meger
Generative diffusion models, notable for their large parameter count (exceeding 100 million) and operation within high-dimensional image spa… (voir plus)ces, pose significant challenges for traditional uncertainty estimation methods due to computational demands. In this work, we introduce an innovative framework, Diffusion Ensembles for Capturing Uncertainty (DECU), designed for estimating epistemic uncertainty for diffusion models. The DECU framework introduces a novel method that efficiently trains ensembles of conditional diffusion models by incorporating a static set of pre-trained parameters, drastically reducing the computational burden and the number of parameters that require training. Additionally, DECU employs Pairwise-Distance Estimators (PaiDEs) to accurately measure epistemic uncertainty by evaluating the mutual information between model outputs and weights in high-dimensional spaces. The effectiveness of this framework is demonstrated through experiments on the ImageNet dataset, highlighting its capability to capture epistemic uncertainty, specifically in under-sampled image classes.
2024-09-12
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence (publié)
doi.org
openreview.net
Are Heterophily-Specific GNNs and Homophily Metrics Really Effective? Evaluation Pitfalls and New Benchmarks
Sitao Luan
Qincheng Lu
Chenqing Hua
Xinyu Wang
Jiaqi Zhu
Xiao-Wen Chang
Guy Wolf
Jian Tang
Over the past decade, Graph Neural Networks (GNNs) have achieved great success on machine learning tasks with relational data. However, rece… (voir plus)nt studies have found that heterophily can cause significant performance degradation of GNNs, especially on node-level tasks. Numerous heterophilic benchmark datasets have been put forward to validate the efficacy of heterophily-specific GNNs and various homophily metrics have been designed to help people recognize these malignant datasets. Nevertheless, there still exist multiple pitfalls that severely hinder the proper evaluation of new models and metrics. In this paper, we point out three most serious pitfalls: 1) a lack of hyperparameter tuning; 2) insufficient model evaluation on the real challenging heterophilic datasets; 3) missing quantitative evaluation benchmark for homophily metrics on synthetic graphs. To overcome these challenges, we first train and fine-tune baseline models on
2024-09-09
ArXiv (prépublication)
doi.org
arxiv.org
Correction: Economic evaluation of the effect of needle and syringe programs on skin, soft tissue, and vascular infections in people who inject drugs: a microsimulation modelling approach
Jihoon Lim
W Alton Russell
Mariam El-Sheikh
David Buckeridge
Dimitra Panagiotoglou
2024-09-09
Harm Reduction Journal (publié)
doi.org